This part includes descriptive statistics after feature extraction, data cleaning, and preparation.
########## folders ##########
# current folder (first go to session -> set working directory -> to source file location)
parentfolder <- dirname(getwd())
data <- paste0(parentfolder, '/MultIS_data/')
audiodata <- paste0(parentfolder, '/audio_processed/')
syllables <- paste0(audiodata, 'syllables/')
dataworkspace <- paste0(parentfolder, '/data_processed/')
datamerged <- paste0(parentfolder, '/data_merged/')
datasets <- paste0(parentfolder, '/datasets/')
models <- paste0(parentfolder, '/models/')
plots <- paste0(parentfolder, '/plots/')
scripts <- paste0(parentfolder, '/scripts/')
########## source file ##########
#source(paste0(scripts, "adjectives-preparation.R"))
#################### packages ####################
# Data Manipulation
library(tibble)
library(stringr)
library(tidyverse) # includes readr, tidyr, dplyr, ggplot2
# Plotting
library(ggforce)
library(ggpubr)
library(gridExtra)
colorBlindBlack8 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
participant_info <- read_delim(paste0(data,"ParticipantInfo_GERCAT.csv"), delim = ";")
# Load the information about duration of each segment (if needed)
data_df <- read.table(paste0(syllables, "fileDurationsDF.csv"), header = TRUE, sep = ',')
# Load cleaned syllable data
data <- read_csv(paste0(datasets, "data_cleaned.csv"))
# Load cleaned targets data
targets <- read_csv(paste0(datasets, "targets.csv"))
# Load cleaned targets with pre-post data
data_prepost <- read_csv(paste0(datasets, "data_prepost.csv"))
# Process participant_info so that participant number column is only number
participant_info$Participant <- parse_number(participant_info$Participant)
# Merge the dataframes by "Participant" and "Language"
# Exchange META to the dataframe of your liking
# META <- merge(META, participant_info, by = c("Participant", "Language"), all.x = TRUE)
How many NAs are there?
# Columns to process
columns_to_process <- c(
"duration", "duration_noSilence", "ampl_median", "ampl_noSilence_median", "env_slope",
"pitch_median", "pitch_sd", "f0_slope", "f1_freq_median", "f2_freq_median",
"specCentroid_median", "entropy_median", "HNR_median", "amEnvDep_median", "fmDep_median",
"pitch_median_norm", "pitch_sd_norm", "f0_slope_norm", "f1_freq_median_norm", "f2_freq_median_norm",
"syllTextPre", "durationPre", "duration_noSilencePre", "ampl_medianPre",
"ampl_noSilence_medianPre", "env_slopePre", "pitch_medianPre", "pitch_sdPre", "f0_slopePre",
"f1_freq_medianPre", "f2_freq_medianPre", "specCentroid_medianPre", "entropy_medianPre", "HNR_medianPre",
"amEnvDep_medianPre", "fmDep_medianPre", "pitch_median_normPre", "pitch_sd_normPre", "f0_slope_normPre",
"f1_freq_median_normPre", "f2_freq_median_normPre", "syllTextPost", "durationPost", "duration_noSilencePost",
"ampl_medianPost", "ampl_noSilence_medianPost", "env_slopePost", "pitch_medianPost", "pitch_sdPost", "f0_slopePost",
"f1_freq_medianPost", "f2_freq_medianPost", "specCentroid_medianPost", "entropy_medianPost", "HNR_medianPost",
"amEnvDep_medianPost", "fmDep_medianPost", "pitch_median_normPost", "pitch_sd_normPost", "f0_slope_normPost",
"f1_freq_median_normPost", "f2_freq_median_normPost"
)
# Ensure columns to process are numeric
columns_to_process <- columns_to_process[columns_to_process %in% names(data_prepost)]
columns_to_process <- columns_to_process[sapply(data_prepost[columns_to_process], is.numeric)]
# Function to calculate raw number and proportion of NAs
calculate_na_stats <- function(df, columns) {
na_counts <- colSums(is.na(df[, columns]))
total_counts <- nrow(df)
proportions <- na_counts / total_counts * 100
return(data.frame("NA_Count" = na_counts, "Proportion" = proportions))
}
# Initial NA stats
na_stats_before <- calculate_na_stats(data_prepost, columns_to_process)
print(na_stats_before)
## NA_Count Proportion
## duration 0 0.00000000
## duration_noSilence 1 0.02178649
## ampl_median 1 0.02178649
## ampl_noSilence_median 1 0.02178649
## env_slope 44 0.95860566
## pitch_median 288 6.27450980
## pitch_sd 288 6.27450980
## f0_slope 228 4.96732026
## f1_freq_median 1 0.02178649
## f2_freq_median 1 0.02178649
## specCentroid_median 1 0.02178649
## entropy_median 1 0.02178649
## HNR_median 95 2.06971678
## amEnvDep_median 1 0.02178649
## fmDep_median 461 10.04357298
## pitch_median_norm 288 6.27450980
## pitch_sd_norm 288 6.27450980
## f0_slope_norm 228 4.96732026
## f1_freq_median_norm 1 0.02178649
## f2_freq_median_norm 1 0.02178649
## durationPre 200 4.35729847
## duration_noSilencePre 200 4.35729847
## ampl_medianPre 200 4.35729847
## ampl_noSilence_medianPre 200 4.35729847
## env_slopePre 351 7.64705882
## pitch_medianPre 658 14.33551198
## pitch_sdPre 658 14.33551198
## f0_slopePre 676 14.72766885
## f1_freq_medianPre 200 4.35729847
## f2_freq_medianPre 200 4.35729847
## specCentroid_medianPre 200 4.35729847
## entropy_medianPre 200 4.35729847
## HNR_medianPre 357 7.77777778
## amEnvDep_medianPre 200 4.35729847
## fmDep_medianPre 1217 26.51416122
## pitch_median_normPre 658 14.33551198
## pitch_sd_normPre 658 14.33551198
## f0_slope_normPre 676 14.72766885
## f1_freq_median_normPre 200 4.35729847
## f2_freq_median_normPre 200 4.35729847
## durationPost 318 6.92810458
## duration_noSilencePost 318 6.92810458
## ampl_medianPost 318 6.92810458
## ampl_noSilence_medianPost 318 6.92810458
## env_slopePost 405 8.82352941
## pitch_medianPost 1040 22.65795207
## pitch_sdPost 1040 22.65795207
## f0_slopePost 897 19.54248366
## f1_freq_medianPost 318 6.92810458
## f2_freq_medianPost 318 6.92810458
## specCentroid_medianPost 318 6.92810458
## entropy_medianPost 318 6.92810458
## HNR_medianPost 631 13.74727669
## amEnvDep_medianPost 318 6.92810458
## fmDep_medianPost 1356 29.54248366
## pitch_median_normPost 1040 22.65795207
## pitch_sd_normPost 1040 22.65795207
## f0_slope_normPost 897 19.54248366
## f1_freq_median_normPost 318 6.92810458
## f2_freq_median_normPost 318 6.92810458
How many target syllables do we have per language?
targets %>%
group_by(language) %>%
summarize(Cumulative_Count = n())
And how are they distributed across perceived prosodic prominence ratings?
syll_per_pros <-
targets %>%
group_by(language, percProm) %>%
summarize(Count = n()) %>%
mutate(Proportion = Count / sum(Count))
## Count
ggplot(syll_per_pros, aes(x = percProm, y = Count, fill = language)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.7) +
labs(#title = "Count of Syll per Language and Prominence",
x = "Perceived prominence", y = "Count") +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_count.png"), plot = last_plot(), width = 6, height = 4)
## Proportion
ggplot(syll_per_pros, aes(x = percProm, y = Proportion, fill = language)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.7) +
labs(#title = "Proportion of Syll per Language and Prominence",
x = "Perceived prominence", y = "Proportion") +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_prop.png"), plot = last_plot(), width = 6, height = 4)
What is the average duration across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_duration = mean(duration, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = duration, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Duration (total)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_duration.png"), plot = last_plot(), width = 6, height = 4)
What is the average duration of sounding across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_duration_noSilence = mean(duration_noSilence, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = duration_noSilence, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Duration (without silences)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_duration_noSilence.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_ampl_median = mean(ampl_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = ampl_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_ampl_median = mean(ampl_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = ampl_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (median) without silences across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_ampl_noSilence_median = mean(ampl_noSilence_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = ampl_noSilence_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude without silences (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_noSilence_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (sd) without silences across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_ampl_noSilence_median = mean(ampl_noSilence_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = ampl_noSilence_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude without silences (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_noSilence_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude envelope slope across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_env_slope = mean(env_slope, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = env_slope, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude envelope slope",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_env_slope.png"), plot = last_plot(), width = 6, height = 4)
What is the average pitch (median) across the different prosodic prominence ratings in Catalan vs in German?
# Raw
targets %>%
group_by(language, percProm) %>%
summarize(avg_pitch_median = mean(pitch_median, na.rm = TRUE))
# Normalized
targets %>%
group_by(language, percProm) %>%
summarize(avg_pitch_median = mean(pitch_median_norm, na.rm = TRUE))
Let’s plot it.
# Raw
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = pitch_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f0 (raw medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_median_raw.png"), plot = last_plot(), width = 6, height = 4)
# Normalized
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = pitch_median_norm, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f0 (normalized medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_median_norm.png"), plot = last_plot(), width = 6, height = 4)
What is the average pitch (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Raw
targets %>%
group_by(language, percProm) %>%
summarize(avg_pitch_sd = mean(pitch_sd, na.rm = TRUE))
# Normalized
targets %>%
group_by(language, percProm) %>%
summarize(avg_pitch_sd = mean(pitch_sd_norm, na.rm = TRUE))
Let’s plot it.
# Raw
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = pitch_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f0 (raw sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_sd_raw.png"), plot = last_plot(), width = 6, height = 4)
# Normalized
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = pitch_sd_norm, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f0 (normalized sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_sd_norm.png"), plot = last_plot(), width = 6, height = 4)
What is the average f0 slope across the different prosodic prominence ratings in Catalan vs in German?
# Raw
targets %>%
group_by(language, percProm) %>%
summarize(avg_f0_slope = mean(f0_slope, na.rm = TRUE))
# Normalized
targets %>%
group_by(language, percProm) %>%
summarize(avg_f0_slope = mean(f0_slope_norm, na.rm = TRUE))
Let’s plot it.
# Raw
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = f0_slope, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f0 slope (raw)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f0_slope_raw.png"), plot = last_plot(), width = 6, height = 4)
# Normalized
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = f0_slope_norm, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f0 slope (normalized)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f0_slope_norm.png"), plot = last_plot(), width = 6, height = 4)
What is the average f1 (median) across the different prosodic prominence ratings in Catalan vs in German?
# Raw
targets %>%
group_by(language, percProm) %>%
summarize(avg_f1_freq_median = mean(f1_freq_median, na.rm = TRUE))
# Normalized
targets %>%
group_by(language, percProm) %>%
summarize(avg_f1_freq_median = mean(f1_freq_median_norm, na.rm = TRUE))
Let’s plot it.
# Raw
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = f1_freq_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f1 (raw medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f1_freq_median_raw.png"), plot = last_plot(), width = 6, height = 4)
# Normalized
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = f1_freq_median_norm, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f1 (normalized medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f1_freq_median_norm.png"), plot = last_plot(), width = 6, height = 4)
We will not use f1 because we cannot be sure that the distribution of vowels is even across perceived prominence ratings.
What is the average f2 (median) across the different prosodic prominence ratings in Catalan vs in German?
# Raw
targets %>%
group_by(language, percProm) %>%
summarize(avg_f2_freq_median = mean(f2_freq_median, na.rm = TRUE))
# Normalized
targets %>%
group_by(language, percProm) %>%
summarize(avg_f2_freq_median = mean(f2_freq_median_norm, na.rm = TRUE))
Let’s plot it.
# Raw
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = f2_freq_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f2 (raw medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f2_freq_median_raw.png"), plot = last_plot(), width = 6, height = 4)
# Normalized
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = f2_freq_median_norm, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "f2 (normalized medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f2_freq_median_norm.png"), plot = last_plot(), width = 6, height = 4)
We will not use f2 because we cannot be sure that the distribution of vowels is even across perceived prominence ratings.
What is the average CPP (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_CPP_median = mean(CPP_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = CPP_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "CPP (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_CPP_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average CPP (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_CPP_sd = mean(CPP_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = CPP_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "CPP (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_CPP_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average flux (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_flux_median = mean(flux_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = flux_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Flux (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
ylim(0, 0.1) + # Because of outliers, especially in Catalan
theme_minimal()
ggsave(filename = paste0(plots, "prominence_flux_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average flux (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_flux_sd = mean(flux_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = flux_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Flux (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_flux_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average novelty (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_novelty_median = mean(novelty_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = novelty_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Novelty (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_novelty_median.pdf"), plot = last_plot(), width = 6, height = 4)
What is the average novelty (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_novelty_sd = mean(novelty_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = novelty_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Novelty (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_novelty_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average spectral centroid (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_specCentroid_median = mean(specCentroid_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = specCentroid_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Spectral centroid (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_specCentroid_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average spectral centroid (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_specCentroid_sd = mean(specCentroid_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = specCentroid_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Spectral centroid (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_specCentroid_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average Weiner entropy (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_entropy_median = mean(entropy_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = entropy_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Weiner Entropy (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropy_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average Weiner entropy (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_entropy_sd = mean(entropy_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = entropy_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Weiner Entropy (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropy_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average Shannon entropySh (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_entropySh_median = mean(entropySh_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = entropySh_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Shannon Entropy (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropySh_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average Shannon entropySh (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_entropySh_sd = mean(entropySh_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = entropySh_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Shannon Entropy (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropySh_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average Harmonics-to-Noise Ratio (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_HNR_median = mean(HNR_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = HNR_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "HNR (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_HNR_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average Harmonics-to-Noise Ratio (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_HNR_sd = mean(HNR_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = HNR_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "HNR (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_HNR_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude modulation (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_amEnvDep_median = mean(amEnvDep_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = amEnvDep_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude modulation (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_amEnvDep_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude modulation (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_amEnvDep_sd = mean(amEnvDep_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = amEnvDep_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Amplitude modulation (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_amEnvDep_sd.png"), plot = last_plot(), width = 6, height = 4)
What is the average frequency modulation (median) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_fmDep_median = mean(fmDep_median, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = fmDep_median, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Frequency modulation (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_fmDep_median.png"), plot = last_plot(), width = 6, height = 4)
What is the average frequency modulation (sd) across the different prosodic prominence ratings in Catalan vs in German?
targets %>%
group_by(language, percProm) %>%
summarize(avg_fmDep_sd = mean(fmDep_sd, na.rm = TRUE))
Let’s plot it.
ggplot(targets %>% filter(!is.na(percProm)), aes(x = language, y = fmDep_sd, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Frequency modulation (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
ylim(-0.1, 0.5) + # because of outliers
theme_minimal()
ggsave(filename = paste0(plots, "prominence_fmDep_sd.png"), plot = last_plot(), width = 6, height = 4)
Prepare data frame.
data_long <- data_prepost %>%
select(fileName, language, itemType, focus, annotationNum,
starts_with("pitch_"),
starts_with("env_"),
starts_with("duration"),
starts_with("ampl_"),
starts_with("f0_"),
starts_with("f1_freq_"),
starts_with("f2_freq_"),
starts_with("CPP_"),
starts_with("flux_"),
starts_with("novelty_"),
starts_with("specCentroid_"),
starts_with("entropy_"),
starts_with("entropySh_"),
starts_with("HNR_"),
starts_with("amEnvDep_"),
starts_with("fmDep_")) %>%
pivot_longer(cols = -c(fileName, language, itemType, focus, annotationNum),
names_to = "variable",
values_to = "value") %>%
mutate(
phase = case_when(
grepl("Pre$", variable) ~ "pre",
grepl("Post$", variable) ~ "post",
TRUE ~ "target"
),
# Remove suffixes from variable names for a cleaner look
variable = gsub("Pre|Post", "", variable)
)
# Correct the phase factor levels
data_long$phase <- factor(data_long$phase, levels = c("pre", "target", "post"))
# Get unique languages and variables
languages <- unique(data_long$language)
variables <- unique(data_long$variable)
# Without lines
for (var in variables) {
for (lang in languages) {
# Filter data_long for the current language and variable
data_filtered <- subset(data_long, variable == var & language == lang)
# Generate the plot for the current language and variable
p <- ggplot(data = data_filtered, aes(x = phase, y = value, fill = phase)) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
scale_fill_manual(values = colorBlindBlack8) +
labs(x = "Syllable",
y = var,
title = lang) +
theme_minimal() +
theme(legend.position = "none")
# Dynamically generate the file name to include both language and variable
file_name <- paste0(plots, "/prepost_", var, "_", lang, ".png")
# Save the plot
ggsave(filename = file_name, plot = p, width = 10, height = 8)
}
}
# With lines
for (var in variables) {
for (lang in languages) {
# Filter data_long for the current language and variable
data_filtered <- subset(data_long, variable == var & language == lang)
# Generate the plot for the current language and variable
p <- ggplot(data = data_filtered, aes(x = phase, y = value, fill = phase)) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
geom_line(aes(group = interaction(fileName, annotationNum)), color = "grey", alpha = 0.1) +
scale_fill_manual(values = colorBlindBlack8) +
labs(x = "Syllable",
y = var,
title = lang) +
theme_minimal() +
theme(legend.position = "none")
# Dynamically generate the file name to include both language and variable
file_name <- paste0(plots, "/prepost_", var, "_lines_", lang, ".png")
# Save the plot
ggsave(filename = file_name, plot = p, width = 10, height = 8)
}
}
# Clean up the environment
rm(languages, lang, variables, var, data_filtered)
# Define unique variables for plotting
variables <- unique(data_long$variable)
# Without lines
for (var in variables) {
# Filter df_long for the current variable
data_filtered <- subset(data_long, variable == var)
# Generate the plot for the current variable
p <- ggplot(data = data_filtered, aes(x = phase, y = value, fill = language)) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
scale_fill_manual(values = colorBlindBlack8) +
labs(x = "Syllable",
y = var) +
theme_minimal()
# Dynamically generate the file name to include the variable
file_name <- paste0(plots, "prepost_", var, ".png")
# Save the plot
ggsave(filename = file_name, plot = p, width = 10, height = 8)
}
## Warning: Removed 1986 rows containing non-finite outside the scale range
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## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
# With lines
for (var in variables) {
# Filter df_long for the current variable
data_filtered <- subset(data_long, variable == var)
# Generate the plot for the current variable
p <- ggplot(data = data_filtered, aes(x = phase, y = value, fill = language)) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
geom_line(aes(group = interaction(fileName, annotationNum)), color = "grey", alpha = 0.1) +
scale_fill_manual(values = colorBlindBlack8) +
labs(x = "Syllable",
y = var) +
theme_minimal()
# Dynamically generate the file name to include the variable
file_name <- paste0(plots, "prepost_", var, "_lines", ".png")
# Save the plot
ggsave(filename = file_name, plot = p, width = 10, height = 8)
}
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1916 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1916 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1916 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1916 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 800 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 800 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 781 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 518 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 518 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1801 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1801 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1729 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1801 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1801 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1729 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1916 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1986 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1916 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 522 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 522 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 521 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1083 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1083 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1045 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 1520 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 1520 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 1465 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 519 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 518 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 2883 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 3034 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 2883 rows containing missing values or values outside the scale range
## (`geom_line()`).
# Clean up
rm(variables, var, data_filtered)
What is the average duration of pretonic across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_duration = mean(durationPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = durationPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic duration (total)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_duration_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average duration of sounding across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_duration_noSilence = mean(duration_noSilencePre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = duration_noSilencePre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic duration (without silences)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_duration_noSilence_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_median = mean(ampl_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic amplitude (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_sd = mean(ampl_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic amplitude (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (median) without silences across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_noSilence_median = mean(ampl_noSilence_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_noSilence_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic amplitude without silences (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_noSilence_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (sd) without silences across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_noSilence_sd = mean(ampl_noSilence_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_noSilence_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic amplitude without silences (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_noSilence_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude envelope slope across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_env_slope = mean(env_slopePre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = env_slopePre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
x = "Language",
y = "Pretonic amplitude envelope slope",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_env_slope_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average pitch (median) across the different prosodic prominence ratings in Catalan vs in German?
#Vavlues
## Raw
avg_pitch_median_pre <- data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_median = mean(pitch_medianPre, na.rm = TRUE))
## Normalized
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_median_norm = mean(pitch_median_normPre, na.rm = TRUE))
# Plots
## Raw
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
x = "Language",
y = "Pretonic f0 (raw medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_median_raw_pretonic.png"), plot = last_plot(), width = 6, height = 4)
## Normalized
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_median_normPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
x = "Language",
y = "Pretonic f0 (normalized medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_median_norm_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average pitch (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
## Raw
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_sd = mean(pitch_sdPre, na.rm = TRUE))
## Normalized
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_sd = mean(pitch_sd_normPre, na.rm = TRUE))
# Plots
## Raw
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic f0 (raw sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_sd_raw_pretonic.png"), plot = last_plot(), width = 6, height = 4)
## Normalized
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_sd_normPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic f0 (normalized sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_sd_norm_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average f0 slope across the different prosodic prominence ratings in Catalan vs in German?
#Values
## Raw
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_f0_slope = mean(f0_slopePre, na.rm = TRUE))
## Normalized
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_f0_slope = mean(f0_slope_normPre, na.rm = TRUE))
# Plots
## Raw
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = f0_slopePre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic f0 slope (raw)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f0_slope_raw_pretonic.png"), plot = last_plot(), width = 6, height = 4)
## Normalized
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = f0_slope_normPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic f0 slope (normalized)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f0_slope_norm_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average CPP (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_CPP_median = mean(CPP_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = CPP_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic CPP (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_CPP_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average CPP (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_CPP_sd = mean(CPP_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = CPP_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic CPP (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_CPP_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average flux (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_flux_median = mean(flux_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = flux_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic flux (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
ylim(-0.025, 0.15) + # because of outliers
theme_minimal()
ggsave(filename = paste0(plots, "prominence_flux_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average flux (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_flux_sd = mean(flux_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = flux_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic flux (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_flux_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average novelty (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_novelty_median = mean(novelty_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = novelty_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic novelty (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_novelty_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average novelty (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_novelty_sd = mean(novelty_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = novelty_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic novelty (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_novelty_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average spectral centroid (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_specCentroid_median = mean(specCentroid_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = specCentroid_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic spectral centroid (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_specCentroid_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average spectral centroid (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_specCentroid_sd = mean(specCentroid_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = specCentroid_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic spectral centroid (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_specCentroid_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Weiner entropy (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropy_median = mean(entropy_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropy_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic Weiner entropy (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropy_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Weiner entropy (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropy_sd = mean(entropy_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropy_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic Weiner entropy (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropy_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Shannon entropy (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropySh_median = mean(entropySh_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropySh_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic Shannon entropy (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropySh_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Shannon entropy (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropySh_sd = mean(entropySh_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropySh_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic Shannon entropy (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropySh_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Harmonics-to-Noise Ratio (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_HNR_median = mean(HNR_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = HNR_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic HNR (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_HNR_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Harmonics-to-Noise Ratio (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_HNR_sd = mean(HNR_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = HNR_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic HNR (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_HNR_sd_pretonic.pdf"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude modulation (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_amEnvDep_median = mean(amEnvDep_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = amEnvDep_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic amplitude modulation (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_amEnvDep_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude modulation (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_amEnvDep_sd = mean(amEnvDep_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = amEnvDep_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic amplitude modulation (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_amEnvDep_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average frequency modulation (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_fmDep_median = mean(fmDep_medianPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = fmDep_medianPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic frequency modulation (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_fmDep_median_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average frequency modulation (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_fmDep_sd = mean(fmDep_sdPre, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = fmDep_sdPre, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Pretonic frequency modulation (sds)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
ylim(-0.07, 0.5) + #because of outliers
theme_minimal()
ggsave(filename = paste0(plots, "prominence_fmDep_sd_pretonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average duration of pretonic across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_duration = mean(durationPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = durationPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic duration (total)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_duration_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average duration of sounding across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_duration_noSilence = mean(duration_noSilencePost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = duration_noSilencePost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic duration (without silences)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_duration_noSilence_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_median = mean(ampl_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic amplitude (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_sd = mean(ampl_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic amplitude (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (median) without silences across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_noSilence_median = mean(ampl_noSilence_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_noSilence_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic amplitude without silences (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_noSilence_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude (sd) without silences across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_ampl_noSilence_sd = mean(ampl_noSilence_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = ampl_noSilence_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic amplitude without silences (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_ampl_noSilence_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude envelope slope across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_env_slope = mean(env_slopePost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = env_slopePost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
x = "Language",
y = "Posttonic amplitude envelope slope",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_env_slope_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average pitch (median) across the different prosodic prominence ratings in Catalan vs in German?
#Vavlues
## Raw
avg_pitch_median_pre <- data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_median = mean(pitch_medianPost, na.rm = TRUE))
## Normalized
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_median_norm = mean(pitch_median_normPost, na.rm = TRUE))
# Plots
## Raw
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
x = "Language",
y = "Posttonic f0 (raw medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_median_raw_posttonic.png"), plot = last_plot(), width = 6, height = 4)
## Normalized
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_median_normPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
x = "Language",
y = "Posttonic f0 (normalized medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_median_norm_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average pitch (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
## Raw
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_sd = mean(pitch_sdPost, na.rm = TRUE))
## Normalized
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_pitch_sd = mean(pitch_sd_normPost, na.rm = TRUE))
# Plots
## Raw
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic f0 (raw sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_sd_raw_posttonic.png"), plot = last_plot(), width = 6, height = 4)
## Normalized
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = pitch_sd_normPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic f0 (normalized sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_pitch_sd_norm_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average f0 slope across the different prosodic prominence ratings in Catalan vs in German?
#Values
## Raw
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_f0_slope = mean(f0_slopePost, na.rm = TRUE))
## Normalized
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_f0_slope = mean(f0_slope_normPost, na.rm = TRUE))
# Plots
## Raw
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = f0_slopePost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic f0 slope (raw)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f0_slope_raw_posttonic.png"), plot = last_plot(), width = 6, height = 4)
## Normalized
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = f0_slope_normPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic f0 slope (normalized)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_f0_slope_norm_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average CPP (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_CPP_median = mean(CPP_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = CPP_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic CPP (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_CPP_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average CPP (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_CPP_sd = mean(CPP_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = CPP_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic CPP (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_CPP_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average flux (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_flux_median = mean(flux_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = flux_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic flux (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
ylim(-0.025, 0.15) + # because of outliers
theme_minimal()
ggsave(filename = paste0(plots, "prominence_flux_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average flux (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_flux_sd = mean(flux_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = flux_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic flux (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_flux_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average novelty (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_novelty_median = mean(novelty_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = novelty_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic novelty (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_novelty_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average novelty (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_novelty_sd = mean(novelty_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = novelty_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic novelty (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_novelty_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average spectral centroid (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_specCentroid_median = mean(specCentroid_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = specCentroid_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic spectral centroid (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_specCentroid_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average spectral centroid (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_specCentroid_sd = mean(specCentroid_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = specCentroid_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic spectral centroid (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_specCentroid_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Weiner entropy (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropy_median = mean(entropy_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropy_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic Weiner entropy (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropy_median_postonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Weiner entropy (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropy_sd = mean(entropy_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropy_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic Weiner entropy (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropy_sd_postonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Shannon entropy (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropySh_median = mean(entropySh_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropySh_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic Shannon entropy (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropySh_median_postonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Shannon entropy (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_entropySh_sd = mean(entropySh_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = entropySh_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic Shannon entropy (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_entropySh_sd_postonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Harmonics-to-Noise Ratio (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_HNR_median = mean(HNR_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = HNR_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic HNR (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_HNR_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average Harmonics-to-Noise Ratio (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_HNR_sd = mean(HNR_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = HNR_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic HNR (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_HNR_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude modulation (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_amEnvDep_median = mean(amEnvDep_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = amEnvDep_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic amplitude modulation (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_amEnvDep_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average amplitude modulation (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_amEnvDep_sd = mean(amEnvDep_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = amEnvDep_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic amplitude modulation (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_amEnvDep_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average frequency modulation (median) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_fmDep_median = mean(fmDep_medianPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = fmDep_medianPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic frequency modulation (medians)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
theme_minimal()
ggsave(filename = paste0(plots, "prominence_fmDep_median_posttonic.png"), plot = last_plot(), width = 6, height = 4)
What is the average frequency modulation (sd) across the different prosodic prominence ratings in Catalan vs in German?
# Values
data_prepost %>%
group_by(language, percProm) %>%
summarize(avg_fmDep_sd = mean(fmDep_sdPost, na.rm = TRUE))
# Plot
ggplot(data_prepost %>% filter(!is.na(percProm)), aes(x = language, y = fmDep_sdPost, fill = as.factor(percProm))) +
geom_violin(scale = "width", trim = FALSE, alpha = 0.3) +
geom_boxplot(width = 0.1, outlier.shape = NA, position = position_dodge(width = 0.9), alpha = 0.5) +
labs(
#title = "Duration of Prosodic Prominence Ratings by Language",
x = "Language",
y = "Posttonic frequency modulation (sd)",
fill = "Prosodic prominence"
) +
scale_fill_manual(values = colorBlindBlack8) +
ylim(-0.1, 0.6) + # because of outliers
theme_minimal()
ggsave(filename = paste0(plots, "prominence_fmDep_sd_posttonic.png"), plot = last_plot(), width = 6, height = 4)